Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5095815 | Journal of Econometrics | 2015 | 12 Pages |
Abstract
We address the problem of estimating generalized linear models when some covariate values are missing but imputations are available to fill-in the missing values. This situation generates a bias-precision trade-off in the estimation of the model parameters. Extending the generalized missing-indicator method proposed by Dardanoni et al. (2011) for linear regression, we handle this trade-off as a problem of model uncertainty using Bayesian averaging of classical maximum likelihood estimators (BAML). We also propose a block model averaging strategy that incorporates information on the missing-data patterns and is computationally simple. An empirical application illustrates our approach.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Valentino Dardanoni, Giuseppe De Luca, Salvatore Modica, Franco Peracchi,